Gender Inequality and Income Disparity

This assignment explores the issue of gender inequality in the United Kingdom, particularly examining the relationship between occupation, income, and gender. It presents statistical data and analyses to demonstrate how income levels differ between men and women based on their chosen professions. The report also highlights the significant influence of occupation on income and emphasizes the existence of a gender pay gap in the UK. Furthermore, it discusses the potential implications of these findings for government policy and strategies aimed at addressing gender inequality.

Research Methods

Table of Contents
INRODUCTION..............................................................................................................................1
Question 1: Assessing the extent to which new immigrants are more likely to be poor.............1
Question 2: Assessing gender inequality in terms of income......................................................4
CONCLUSION..............................................................................................................................11
REFERENCES..............................................................................................................................12

INRODUCTION
Research methods, tools and techniques are highly significant which in turn helps scholar
in presenting the fair solution of issue. In the field of research, techniques are highly varied in
line with the type selected such as qualitative and quantitative. Thus, scholar is required to keep
in mind research types when making selection of data collection and analysis tool. The present
report is based on the case scenarios which will provide deeper insight about the level to which
new immigrants are poor. Besides this, it will shed light on level of gender inequality on the
basis of income. Thus, report will develop understanding about the manner in which regression,
correlation, descriptive statistics and chi-square test helps in decision making.
Question 1: Assessing the extent to which new immigrants are more likely to be poor
Descriptive statistics of household data
Particulars
HHRef
UHSIZ
E
DJHHIN
C
NEWIMM
G
TENUR
E
Mean 225161244.2 3.0 65161.6 0.6 2.5
Standard Error 1634703.1 0.0 4072.4 0.0 0.0
Median 224149901 3 21622.5 1 3
Mode #N/A 2 999999 1 3
Standard
Deviation 73106143.02 1.97731 182125 0.48463 1.24456
Sample
Variance 5.34E+15 3.90975 3.3E+10 0.23487 1.54893
Kurtosis -0.8 349.2 21.7 -1.7 -0.9
Skewness 0.185790927 12.7073 4.79586 -0.5102 0.26961
Range 306542600 59 999999 1 4
Minimum 100030701 1 0 0 1
Maximum 406573301 60 999999 1 5
Sum 4.50E+11 5947 1.3E+08 1247 5086
Count 2000 2000 2000 2000 2000
Interpretation: The above depicted table shows that average household income is
£65161.6 respectively. On the other side, income of 50% respondents is £21622.5 significantly.
Further, mode presents that income that is generated by people repeatedly accounts for £999999.
Thus, it can be depicted that monthly income from main employment generated by household is

£999999. In addition to this, NEWIMMIG element provides deeper insight about that if the
household is new immigrant family or not. Mean, mode and median of such element is 0.6, 1 & 1
significantly. It presents that half of the members fall the category of new immigrant. Besides
this, it has been found from evaluation that value of mean, mode and median in the case of tenure
is 2.6, 3 & 3. It exhibits that most of the people included in survey having private ownership.
Analysis of new immigrants is enumerated below:
Row
Labels
Count of
NEWIMMG
0 753
1 1247
Grand
Total 2000
No Half of the family is new
immigrant
0
200
400
600
800
1000
1200
1400
Count of NEWIMMG
Count of NEWIMMG
Interpretation: Graphical presentation shows that out of 2000, 753 come under the
category of no immigrants. In addition to this, half of the families of 1247 respondents are new
immigrants. This aspect presents that level of new immigrants is greater as compared to the
former times.
Evaluation of monthly household income
Row Labels
Count of
DJHHINC
0-9999 445

10000-19999 458
20000-29999 359
30000-39999 207
40000-49999 142
50000-59999 79
60000-69999 52
70000-79999 35
80000-89999 24
90000-99999 21
100000-109999 15
110000-119999 12
120000-129999 15
130000-139999 8
140000-149999 8
150000-159999 49
990000-999999 71
Grand Total 2000
0-9999
10000-19999
20000-29999
30000-39999
40000-49999
50000-59999
60000-69999
70000-79999
80000-89999
90000-99999
100000-109999
110000-119999
120000-129999
130000-139999
140000-149999
150000-159999
990000-999999
0
50
100
150
200
250
300
350
400
450
500
Total
Total
Interpretation: The above depicted graph shows that income level of 445 respondents is
between the ranges of 0-9999. Besides this, 445 households having income between £10000 to
£19999. On the other side, from assessment it has been identified that 359 people having income
within the £20000 to £29999. Further, it has been identified that only 71 respondents having
income of £999999. On the other side, 49 households have income in the mid of £1500000 to
£159999 respectively. Thus, by considering all the above presented aspects it can be said that
majority of the households earn within the ranges of 0 to £59999. Thus, for enhancing the level

of household income and raising standard of living of new immigrant’s government authority is
required to undertake significant measure or action.
Question 2: Assessing gender inequality in terms of income
In order to analyze personal data in an appropriate manner chi-square test has been
applied by the researcher. The rationale behind the selection of such test is that it helps in
evaluating the categorical variables in an effectual way (Kasten, 2017). Besides this, to present
the clear view of whole and large data set descriptive statistic tool has been applied. Hence,
through descriptive statistics tool mean, mode and median is evaluated by the scholar.
Descriptive statistics of personal data
MEARN SEX AGE
TENUR
E
OCCU
P
Mean 506050.30 1.54 41.2 2.46 2.19
Standard Error 8975.18 0.01 0.4 0.02 0.05
Median 150000 2 41.5 3 1
Mode 999999 2 45 3 0
Standard
Deviation 491590.75 0.50 20.9 1.22 2.87
Sample
Variance 2.42E+11 0.25 437.9 1.49 8.26
Kurtosis -2.00
-
1.9701 -0.6 -0.80 0.36
Skewness 0.01
-
0.1768 0.2 0.33 1.22
Range 999999 1 100 4 9
Minimum 0 1 0 1 0
Maximum 999999 2 100 5 9
Sum 1518150911 4632
12365
3 7376 6562

Count 3000 3000 3000 3000 3000
Interpretation: The above depicted table of descriptive statistics present that average
number of respondents who have participated in survey are females. Value of median and mode
is 2 which in turn show that large number of women participated in survey over males. Besides
this, outcome of descriptive statistics show that average income level of respondents is
£506050.30 respectively. Mean and median of age factor accounts for 41.2 & 41.5 respectively.
Further, mode value presents that majority of the respondents are of 45 years. In addition to this,
mean value of tenure accounts for 2.5 which in turn presents that most of the individuals have
private ownership. Further, it can be stated that average number of respondents lie in the
category of professionals and associate. On the other side, 50% of respondents are managers as
well as administrators. From descriptive statistics, it has been found that majority of the people
are unemployed and inactive.
Hypothesis 1:
H0 (Null hypothesis): There is no significant association between the income of men and
women.
H1 (Alternative hypothesis): There is a significant association between the income of men and
women.
Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N Percent
SEX *
MEARN 3000 100.0% 1 0.0% 3001 100.0%

Chi-Square Tests
Value df Asymp. Sig.
(2-sided)
Pearson Chi-Square 470.549a 338 .000
Likelihood Ratio 601.971 338 .000
Linear-by-Linear
Association 21.737 1 .000
N of Valid Cases 3000
a. 613 cells (90.4%) have expected count less than 5. The
minimum expected count is .46.
Interpretation: By applying the tool of chi-square on data set it has been assessed that
value of significance is 0.00. This aspect shows that alternative hypothesis is accepted because
value of significance is within the standard criteria such as 0.05 (Breault and et.al., 2017). Hence,
it can be stated that significant association taken place in income level of men and women. Thus,
irrespective of having strict rules and regulations regarding equal pay inequality exists in the
income level of men and women.
Hypothesis 2:
H0 (Null hypothesis): There is no significant association between the income of men and women
on the basis of age group.
H1 (Alternative hypothesis): There is a significant association between the income of men and
women on the basis of age group.
Case Processing Summary

Cases
Valid Missing Total
N Percent N Percent N Percent
AGE * MEARN 3000 100.0% 1 0.0% 3001 100.0%
OCCUP *
MEARN 3000 100.0% 1 0.0% 3001 100.0%
On the basis of age
Chi-Square Tests
Value df Asymp. Sig.
(2-sided)
Pearson Chi-Square 23273.412a 33462 1.000
Likelihood Ratio 6544.947 33462 1.000
Linear-by-Linear
Association 7.486 1 .006
N of Valid Cases 3000
a. 33816 cells (99.8%) have expected count less than 5. The
minimum expected count is .00.
Interpretation: From quantitative investigation, it has been identified that value of
significance is 1 that entails null hypothesis is accepted over alternative. By taking into account
such aspect, it can be presented that no significant relation exists in the income of men and
women as per the age group (Britten, Thatcher and Caro, 2016).
Hypothesis 3:

H0 (Null hypothesis): There is no significant association between the income of men and women
on the basis of occupation.
H1 (Alternative hypothesis): There is a significant association between the income of men and
women on the basis of occupation.
On the basis of occupation
Chi-Square Tests
Value df Asymp. Sig.
(2-sided)
Pearson Chi-Square 7543.547a 1690 .000
Likelihood Ratio 6174.392 1690 .000
Linear-by-Linear
Association 1756.804 1 .000
N of Valid Cases 3000
a. 1972 cells (97.0%) have expected count less than 5. The
minimum expected count is .05.
Interpretation: Outcome of quantitative analysis shows that value of significance is 0.0
in the case when income of men and women is evaluated on the basis of gender. Generated
outcome through SPSS shows that alternative hypothesis is accepted because it meets standard
criteria to a great extent. By keeping in mind the findings of chi-square test it can be depicted
that occupation and income level of both men as well as women is highly associated with each
other (Gerbig and et.al., 2017). It shows that, income level highly varies as per the occupation of
men and women.
Hypothesis 4: Regression
H0: There is no significant difference in the mean value of income and occupation.
H1: There is a significant difference in the mean value of income and occupation.

Variables Entered/Removeda
Model Variables
Entered
Variables
Removed
Method
1 OCCUPb . Enter
a. Dependent Variable: MEARN
b. All requested variables entered.
Model Summaryb
Model R R Square Adjusted R
Square
Std. Error of
the Estimate
Durbin-
Watson
1 .765a .586 .586 316433.803 1.880
a. Predictors: (Constant), OCCUP
b. Dependent Variable: MEARN
ANOVAa
Model Sum of
Squares
df Mean Square F Sig.

1
Regression 424551938701
400.600 1 424551938701
400.600 4239.992 .000b
Residual 300190794215
155.600 2998 100130351639
.478
Total 724742732916
556.200 2999
a. Dependent Variable: MEARN
b. Predictors: (Constant), OCCUP
Coefficientsa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 792351.681 7260.097 109.138 .000
OCCUP -130890.602 2010.138 -.765 -65.115 .000
a. Dependent Variable: MEARN
Residuals Statisticsa
Minimum Maximum Mean Std. Deviation N
Predicted Value -385663.75 792351.69 506050.30 376250.583 3000
Residual -661461.063 405663.750 .000 316381.042 3000

Std. Predicted
Value -2.370 .761 .000 1.000 3000
Std. Residual -2.090 1.282 .000 1.000 3000
Interpretation: Tabular presentation shows that R and R square is .76 & .59
respectively. By taking into account such aspect it can be said that occupation and
income. Besides this, it can be stated that R square is also higher which shows that if
one variable will changes then other also affected significantly. Besides this, positive
relationship shows that income level is highly dependent on occupation. Further, table
depicted above shows that level of significance is 0.00 which presents that alternative
hypothesis is accepted. Hence, it can be said that income level is highly affected from
the type occupation. Moreover, importance of one occupation highly varies from
another. For instance: manager gets high level of opportunity pertaining to job aspect
as compared to others. Besides this, in health care sector, firm requires highly skilled
and professional candidate which are operating in health care sector. On the other
side, requirement of skilled personnel is less in the case of hospitality and retail
sector.
CONCLUSION
By summing up this report, it has been articulated that level of new
immigrants increased to a great extent as compared to the prior times. Besides this, it
can be inferred that income level of men and women is highly associated with the
occupational aspect. Further, it can be concluded that inequality exists in UK on the
basis of gender. It can be seen in the report that age and income of men as well as
women are not significantly associated. Thus, it can be summarized from the report
that gender inequality exists in UK. Thus, government authority is required to take
strategic action for preventing the issue of gender inequality and pay gap.

REFERENCES
Books and Journals
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analysis. American Journal of Nuclear Medicine and Molecular Imaging. 7(3). p.84.
Britten, K. H., Thatcher, T. D. and Caro, T., 2016. Zebras and biting flies: quantitative analysis
of reflected light from zebra coats in their natural habitat. PloS one. 11(5). p.e0154504.
Gerbig, S. and et.al., 2017. Method development towards qualitative and semi-quantitative
analysis of multiple pesticides from food surfaces and extracts by desorption electrospray
ionization mass spectrometry as a preselective tool for food control. Analytical and
bioanalytical chemistry. 409(8). pp.2107-2117.
Jain, N. and et.al., 2017. Rules of RNA specificity of hnRNP A1 revealed by global and
quantitative analysis of its affinity distribution. Proceedings of the National Academy of
Sciences. p.201616371.
Kasten, L., 2017. When Less Is More: Constructing a Parsimonious Concept of Interstate Peace
for Quantitative Analysis. International Studies Review. 19(1). pp.28-52.